Human-in-the-Loop Causal Discovery with Ancestral GFlowNets: Addressing Latent Confounding and Uncertainty Quantification

核心概念

This paper introduces Ancestral GFlowNets (AGFN), a novel probabilistic causal discovery method that samples ancestral graphs to represent causal relationships while addressing latent confounding and incorporating potentially noisy human feedback.

要約

Bibliographic Information:

da Silva, T., Silva, E., G´ois, A., Heider, D., Kaski, S., Mesquita, D., & Ribeiro, A. (2024). Human-in-the-Loop Causal Discovery under Latent Confounding using Ancestral GFlowNets. arXiv preprint arXiv:2309.12032v2.

Research Objective:

This paper addresses the challenge of causal discovery (CD) under latent confounding, particularly in low-data regimes where traditional CD algorithms struggle. The authors aim to develop a method that quantifies uncertainty in CD and leverages human expertise to refine causal inferences.

Methodology:

The researchers propose Ancestral GFlowNets (AGFN), a novel approach that utilizes Generative Flow Networks (GFlowNets) to sample ancestral graphs (AGs) proportionally to a score function, such as the Bayesian Information Criterion (BIC). This probabilistic approach allows for uncertainty quantification in the inferred causal structures. To further improve accuracy, the authors introduce an active knowledge elicitation framework that queries experts about relationships between variables, using an optimal experimental design strategy to minimize uncertainty. Human feedback is then incorporated into the model via importance sampling, refining the belief distribution over AGs.

Key Findings:

• AGFN accurately learns distributions over AGs, effectively capturing epistemic uncertainty inherent in CD.
• The proposed method outperforms existing probabilistic CD baselines in terms of structural Hamming distance (SHD) and BIC.
• Incorporating simulated human feedback significantly reduces uncertainty and improves the accuracy of the inferred causal structures.
• The active learning strategy, based on minimizing expected cross-entropy, proves more efficient than random querying in reducing uncertainty.

Main Conclusions:

AGFN presents a novel and effective approach for CD under latent confounding, particularly in low-data settings. By combining probabilistic sampling with human-in-the-loop learning, AGFN offers a robust and practical solution for uncovering causal relationships from observational data.

Significance:

This research significantly contributes to the field of CD by introducing a probabilistic framework that addresses key limitations of existing methods. The integration of human expertise through an efficient active learning strategy further enhances the practicality and reliability of causal inference.

Limitations and Future Research:

The current work focuses on linear Gaussian models and utilizes BIC as the score function. Future research could explore the applicability of AGFN to different data types and score functions. Additionally, evaluating the method with real-world expert feedback would further validate its effectiveness in practical applications.

統計

Over 60% of the samples from the existing probabilistic CD method (N-ADMG) were non-ancestral.
AGFN consistently outperforms N-ADMG in terms of both SHD and BIC.
The three most rewarding samples from AGFN are as good as (and sometimes better than) the other CD algorithms.

引用

抽出されたキーインサイト

Human-in-the-Loop Causal Discovery under Latent Confounding using Ancestral GFlowNets

by Tiag... 場所 arxiv.org 11-04-2024

https://arxiv.org/pdf/2309.12032.pdf

深掘り質問

How can AGFN be extended to handle time-series data and dynamic causal relationships?

Extending AGFN to handle time-series data and dynamic causal relationships presents exciting opportunities and requires careful consideration of the temporal dimension inherent in such data. Here's a breakdown of potential approaches:

Adaptation to Dynamic Bayesian Networks (DBNs):

Conceptual Shift: Instead of Ancestral Graphs (AGs), the framework could be adapted to learn the structure of Dynamic Bayesian Networks (DBNs). DBNs are a natural extension of Bayesian Networks for modeling time-series data, representing causal relationships between variables across discrete time slices.
Structural Modifications: AGFN's generative process would need adjustments to construct DBNs. This involves defining actions for adding edges within a time slice (representing contemporaneous relationships) and across time slices (representing temporal dependencies).
Score Function Adaptation: The score function (e.g., BIC) needs modification to account for the temporal structure of DBNs. This might involve penalizing model complexity based on the number of temporal connections.

Incorporating Time-Lagged Dependencies:

Augmenting the Search Space: AGFN could be modified to explore time-lagged relationships between variables. This could involve representing potential edges as connecting variables at different time points (e.g., X(t-1) -> Y(t)).
Feature Representation: The feature representation of the graph used by the GFlowNet should incorporate temporal information. This could involve using time-series features or embeddings that capture the dynamics of individual variables.

Handling Non-Stationarity:

Time-Varying Parameters: Real-world time-series data often exhibit non-stationarity, meaning the underlying causal relationships might change over time. AGFN could be extended to accommodate this by allowing for time-varying parameters in the causal model.
Adaptive Learning: Techniques from online learning or adaptive inference could be integrated into AGFN to update the causal model as new data points become available, enabling it to adapt to evolving causal dynamics.

Challenges and Considerations:

Computational Complexity: Incorporating temporal dependencies significantly expands the search space of possible causal structures, potentially increasing computational demands. Efficient exploration strategies and representations become crucial.
Data Requirements: Learning dynamic causal relationships typically requires more data than static settings. The amount of data needed scales with the complexity of the temporal dependencies and the number of variables.

Could the reliance on a single expert introduce bias, and how could AGFN be adapted to incorporate feedback from multiple experts with varying levels of expertise?

You are absolutely right to point out the potential for bias when relying on a single expert. Here's how AGFN could be adapted to mitigate this and leverage the collective wisdom of multiple experts:

Modeling Multiple Experts:

Expert-Specific Parameters: Instead of a single reliability parameter (π), each expert could be assigned their own π value, reflecting their individual expertise or trustworthiness as assessed by the system or through prior information.
Weighting Expert Opinions: A weighting scheme could be introduced to combine feedback from multiple experts. Experts with higher reliability (π) or those whose opinions align more closely with the data could be given greater weight.

Aggregating Expert Feedback:

Bayesian Approach: A Bayesian framework could be used to combine expert opinions. Each expert's feedback would be treated as evidence, updating a prior distribution over the possible causal structures.
Consensus Building: Techniques for consensus building or opinion aggregation could be employed. This might involve iterative rounds of feedback where experts can see and potentially revise their opinions based on the input of others.

Handling Disagreements:

Identifying Disputed Relations: AGFN could highlight areas of disagreement among experts, drawing attention to relationships where further investigation or data collection might be beneficial.
Exploring Alternative Structures: In cases of significant disagreement, AGFN could generate multiple candidate causal structures, each reflecting different expert perspectives. This allows decision-makers to consider a range of possibilities.

Addressing Expertise Levels:

Expertise-Dependent Queries: The system could tailor its queries to the specific expertise of each expert. Experts with more domain knowledge could be asked about complex relationships, while those with less experience could provide feedback on more straightforward connections.
Incorporating Confidence Levels: Experts could be allowed to express their confidence in their feedback. This information could be integrated into the model, giving more weight to high-confidence responses.

If causal discovery methods like AGFN become increasingly accurate and reliable, how might this impact decision-making processes in fields like healthcare, economics, or social policy?

The increasing accuracy and reliability of causal discovery methods like AGFN hold the potential to revolutionize decision-making processes across various domains by moving beyond mere correlations to uncover underlying causal mechanisms. Here's how:

Healthcare:

Personalized Treatment: By identifying causal relationships between patient characteristics, treatments, and health outcomes, AGFN could pave the way for personalized medicine. This enables tailoring treatments to individual patients based on their unique causal profiles.
Drug Discovery and Development: Understanding the causal mechanisms of diseases can significantly accelerate drug discovery. AGFN can help identify promising drug targets by pinpointing the root causes of diseases.
Public Health Interventions: AGFN can guide the design and implementation of effective public health interventions. By uncovering the causal drivers of health issues, policymakers can develop targeted strategies to improve population health.

Economics:

Policy Evaluation: AGFN can enable more robust policy evaluation by disentangling the causal effects of policies from other factors. This helps policymakers understand the true impact of their decisions and make evidence-based adjustments.
Economic Forecasting: Incorporating causal relationships into economic models can improve their accuracy and reliability. AGFN can help identify leading indicators and predict economic downturns or growth periods more effectively.
Causal Marketing: Understanding the causal links between marketing campaigns and consumer behavior can optimize advertising strategies. AGFN can help businesses target their marketing efforts more effectively and maximize their return on investment.

Social Policy:

Reducing Inequality: AGFN can help identify the root causes of social inequalities, such as poverty, discrimination, or lack of access to education. This knowledge can guide the development of effective policies to address these issues.
Criminal Justice Reform: By uncovering the causal factors contributing to crime, AGFN can inform criminal justice reform efforts. This can lead to more effective crime prevention strategies and fairer sentencing guidelines.
Education Policy: Understanding the causal relationships between educational interventions and student outcomes can improve educational policies. AGFN can help identify effective teaching methods, allocate resources efficiently, and promote educational equity.

Ethical Considerations:

Bias and Fairness: It's crucial to ensure that causal discovery methods are developed and deployed in a fair and unbiased manner. AGFN should be designed to mitigate potential biases in the data or expert feedback.
Transparency and Explainability: The decision-making processes based on AGFN should be transparent and explainable. This helps build trust in the system and ensures that decisions are accountable.
Human Oversight: While AGFN can provide valuable insights, human judgment and ethical considerations should remain central to decision-making, especially in areas with significant social impact.